Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data

Abstract

In this paper, we explore a kind of teaching-oriented temporal analytics on the timing of support in the context of one-on-one math problem-solving coaching. We build the analytical framework upon the human-human multimodal interaction data collected from the naturalist environments. We demonstrated the potential utility of leveraging survival analysis, a class of statistical methods to model time-to-event data, to gain insights into the timing decisions. We shed light on the heterogeneity of coaching decisions as to when to render support in connection to the problem-solving stages, coaching dyads, as well as the pre-intervention event characteristics. This work opens future avenues into a different type of human tutoring study supported by multimodal data, computational models, and statistical frameworks. This model framework may yield useful reflective teaching analytics to tutors, coaches, or teachers when further developed. We also envision that those analyses may ultimately inform the design of AI-supported autonomous agents that could learn the tutorial interaction logic from empirical data.